code: def t5_forward(
      self,
      hidden_states,
      mask=None,
      key_value_states=None,
      position_bias=None,
      past_key_value=None,
      layer_head_mask=None,
      query_length=None,
      use_cache=False,
      output_attentions=False,
      **kwargs,
  ):
      raise_on_head_mask(layer_head_mask)
  
      if output_attentions is True:
          raise ValueError(
              "output_attentions=True can not be supported with BetterTransformer."
          )
      if len(self.pruned_heads) > 0:
          raise ValueError(
              f"Setting `pruned_heads` is unsupported with BetterTransformer, found {self.pruned_heads}."
          )
      batch_size, seq_length = hidden_states.shape[:2]
  
      real_seq_length = seq_length
  
      if past_key_value is not None:
          assert (
              len(past_key_value) == 2
          ), f"past_key_value should have 2 past states: keys and values. Got { len(past_key_value)} past states"
          real_seq_length += (
              past_key_value[0].shape[2]
              if query_length is None
              else query_length
          )
  
      key_length = (
          real_seq_length
          if key_value_states is None
          else key_value_states.shape[1]
      )
  
      def shape(states):
          """projection"""
          return states.view(
              batch_size, -1, self.n_heads, self.key_value_proj_dim
          ).transpose(1, 2)
  
      def unshape(states):
          """reshape"""
          return (
              states.transpose(1, 2)
              .contiguous()
              .view(batch_size, -1, self.inner_dim)
          )
  
      def project(
          hidden_states, proj_layer, key_value_states, past_key_value
      ):
          """projects hidden states correctly to key/query states"""
          if key_value_states is None:
              # self-attn
              # (batch_size, n_heads, seq_length, dim_per_head)
              hidden_states = shape(proj_layer(hidden_states))
          elif past_key_value is None:
              # cross-attn
              # (batch_size, n_heads, seq_length, dim_per_head)
              hidden_states = shape(proj_layer(key_value_states))
  
          if past_key_value is not None:
              if key_value_states is None:
                  # self-attn
                  # (batch_size, n_heads, key_length, dim_per_head)
                  hidden_states = torch.cat(
                      [past_key_value, hidden_states], dim=2
                  )
              elif past_key_value.shape[2] != key_value_states.shape[1]:
                  # checking that the `sequence_length` of the `past_key_value` is the same as
                  # the provided `key_value_states` to support prefix tuning
                  # cross-attn
                  # (batch_size, n_heads, seq_length, dim_per_head)
                  hidden_states = shape(proj_layer(key_value_states))
              else:
                  # cross-attn
                  hidden_states = past_key_value
          return hidden_states
  
      # get query states
      query_states = shape(
          self.q(hidden_states)
      )  # (batch_size, n_heads, seq_length, dim_per_head)
  
      # get key/value states
      key_states = project(
          hidden_states,
          self.k,
          key_value_states,
          past_key_value[0] if past_key_value is not None else None,
      )
      value_states = project(
          hidden_states,
          self.v,
          key_value_states,
          past_key_value[1] if past_key_value is not None else None,
      )
  
      dropout_p = self.dropout if self.training else 0.0
      query_states = self.scale * query_states
      if position_bias is None and not self.has_relative_attention_bias:
          if mask is None:
              attn_output = (
                  torch.nn.functional.scaled_dot_product_attention(
                      query_states,
                      key_states,
                      value_states,
                      attn_mask=None,
                      dropout_p=dropout_p,
                      is_causal=False,
                  )
              )
          elif mask is not None:
              attn_output = (
                  torch.nn.functional.scaled_dot_product_attention(
                      query_states,
                      key_states,
                      value_states,
                      attn_mask=mask,
                      dropout_p=dropout_p,
                      is_causal=False,
                  )
              )
  
      if position_bias is None:
          if not self.has_relative_attention_bias:
              position_bias = torch.zeros(
                  (1, self.n_heads, real_seq_length, key_length),
                  device=value_states.device,
                  dtype=value_states.dtype,
              )
              if self.gradient_checkpointing and self.training:
                  position_bias.requires_grad = True
          else:
              position_bias = self.compute_bias(
                  real_seq_length,
                  key_length,
                  device=value_states.device,
              )
  
          # if key and values are already calculated
          # we want only the last query position bias
          if past_key_value is not None:
              position_bias = position_bias[
                  :, :, -hidden_states.size(1) :, :
              ]
  
          if mask is not None:
              position_bias = (
                  position_bias + mask
              )  # (batch_size, n_heads, seq_length, key_length)
  
          if self.has_relative_attention_bias:
              attn_output = (
                  torch.nn.functional.scaled_dot_product_attention(
                      query_states,
                      key_states,
                      value_states,
                      attn_mask=position_bias,
                      dropout_p=dropout_p,
                      is_causal=False,
                  )
              )
      else:
          attn_output = (
              torch.nn.functional.scaled_dot_product_attention(
                  query_states,
                  key_states,
                  value_states,
                  attn_mask=position_bias,
                  dropout_p=dropout_p,
                  is_causal=False,
              )
          )
  
      attn_output = unshape(
          attn_output
      )  # (batch_size, seq_length, dim)
      attn_output = self.o(attn_output)
  
      present_key_value_state = (
          (key_states, value_states)
          if (self.is_decoder and use_cache)
          else None
      )
      outputs = (
          (attn_output,) + (present_key_value_state,) + (position_bias,)
      )
  
      return outputs
  
file_path: optimum/optimum/bettertransformer/models/attention.py
imports: from typing import Optional, Tuple
  import torch
label_desc: Use image node_id 7 for calling a global function with example usage: t5_forward(hidden_states, mask, key_value_states, position_bias, past_key_value, layer_head_mask, query_length, use_cache, output_attentions) and returns: outputs, states, states, hidden_states